Recently, graph neural networks have achieved promising results in processing graph data [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. Kipf et al. [1]} proposed the Graph Convolution Network (GCN), which propagates node features by spectral graph convolutions. GraphSAGE [2]}
further improved the scalability by sampling neighbor nodes and aggregating their features. Graph Attention Network (GAT) [3]} adopted the attention mechanism to aggregate local features of neighbor nodes with the automatically learned weights. Different from the encouraging progress made in GNNs architecture design, the purpose of this paper is to design a novel online knowledge distillation framework applicable to various GNNs.
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